Methods for operating a wireless network and according networks and devices of the above mentioned type are known from the state of the art. Such methods, networks and devices can use known MIMO techniques.
It is well accepted in the industry and academia that novel wireless techniques are required to cope with the exponential increase in data traffic forecasted for the upcoming years.
As an example of known MIMO techniques SDMA (Space Division Multiple Access) will be explained in the following for stating the present situation of network technology in this regard. SDMA is a novel physical layer technique that can be used for the purpose of increasing capacity, and will be widely adopted by 4G wireless technologies. SDMA requires deploying several antennas at the Base Station (BS), in order to allow the BS to transmit simultaneously to different stations. The previous is achieved by steering different spatial beams towards the intended receivers as depicted in FIG. 1. Particularly, transmission schemes based on Orthogonal Frequency Division Multiple Access (OFDMA) combined with SDMA techniques are key promising technologies to increase current spectral efficiencies.
When using SDMA, a BS needs an algorithm that decides which stations can be multiplexed in space. For instance, if two stations are very close to each other or their channels are highly correlated, then it is impossible for the BS to construct beams to transmit simultaneously to both stations using the same time/frequency resources. In order to decide which subsets of stations can be addressed simultaneously using SDMA, a BS requires an SDMA grouping algorithm.
An SDMA grouping algorithm can be seen as operating in the following way, see T. F. Maciel, Suboptimal resource allocation for multi-user mimo-ofdma systems, Ph.D. dissertation, Technical University of Darmstadt:                1—Select a set of stations to form a candidate SDMA group.        2—Evaluate how good this group is. For the purpose of evaluating a candidate group one requires a grouping metric.        3—From all the evaluated groups select the one that maximizes the chosen grouping metric.        
Therefore a key parameter to have well performing SDMA grouping algorithms is to have a good grouping metric, i.e. a metric that can reliably capture the interference conditions in the group. Ideally, the best grouping metrics are the ones that predict the SINR of each station in the SDMA group, see T. F. Maciel, Suboptimal resource allocation for multi-user mimo-ofdma systems, Ph.D. dissertation, Technical University of Darmstadt. Such metrics are able to identify if a pair or set of stations will heavily interfere with each other when in the same SDMA group, and should therefore be allocated to different groups.
The current problem in the state of the art that this invention addresses is the fact that computing grouping metrics based on the SINR experienced by each station within an SDMA group is computationally very expensive. The reason being that these metrics require computing for each candidate SDMA group, the actual beamforming weights that the BS will use to transmit to the SDMA group. The following formula is a typical way to compute these beamforming weights, e.g. Minimum Mean Square Error technique, see F. Gross, Smart antennas for wireless communications with MATLAB, Professional engineering, McGraw-Hill, 2005:Wb=(Rss+Rnn)−1Hb,where the previous are matrixes and vectors with dimensions that depend on the number of antennas in the BS. Notice that these are expensive operations, e.g. for M antennas in the BS the previous formula requires O(M3) arithmetic operations. Moreover, in a wideband system, like WiMAX or LTE (Long Term Evolution), the channel becomes frequency selective, and therefore the previous computation has to be done per frequency resource block b. Therefore, computing Wb for each candidate SDMA group turns out to be very expensive.